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Record W3152948160 · doi:10.33182/bc.v11i1.1220

The Association Between Afghan Refugees’ Food Insecurity and Socio-economic Factors in Iran: A Case Study of Khorasan Razavi Province

2021· article· en· W3152948160 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBORDER CROSSING · 2021
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of ReginaToronto Metropolitan UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsAfghanRefugeeFood securityFood insecuritySocioeconomicsEnvironmental healthGeographyEconomic growthPolitical scienceMedicineSociologyEconomicsAgriculture

Abstract

fetched live from OpenAlex

Afghan refugees are one of the most vulnerable migrant groups in terms of food insecurity status around the world. We aimed to investigate the association between Afghan protracted refugees' food insecurity and its socio– economic determinants in Mashhad, Iran. In a cross– sectional design, information was gathered through face– to– face interviews with 299 Afghan main income earners or his/her representative in Golshar district, Mashhad, Iran. In a quantitative approach, the association of socio– economic factors with food insecurity was assessed. The results showed that less than 1% of all the households were food secure, 69.2% of those with children and 47.5% of those with no child faced severe food insecurity. Class of households' income, residency status and personal dwelling were significantly associated with severe food insecurity of Afghan refugees. Determining effective socio– economic factors to formulate appropriate policies and practices is not only necessary but also inevitable to assure sustainable food security for refugees.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.133
GPT teacher head0.467
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it